From Hashing to CNNs: Training Binary Weight Networks via Hashing
Authors: Qinghao Hu, Peisong Wang, Jian Cheng
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on CIFAR10, CIFAR100 and Image Net demonstrate that our proposed BWNH outperforms current state-of-art by a large margin. |
| Researcher Affiliation | Academia | Qinghao Hu, Peisong Wang, Jian Cheng Institute of Automation, Chinese Academy of Sciences, Beijing, China University of Chinese Academy of Sciences, Beijing, China Center for Excellence in Brain Science and Intelligence Technology, CAS, Beijing, China |
| Pseudocode | Yes | Algorithm 1: Training Binary weight Convolutional Neural Networks via Hashing |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | Yes | To evaluate our proposed method, we conduct extensive experiments on three public benchmark datasets including CIFAR10, CIFAR100, and Image Net. ... Image Net dataset (ILSVRC2012) has about 1.2M training images from 1000 classes and 50,000 validation images. |
| Dataset Splits | Yes | CIFAR10 dataset consists of 60,000 colour images in 10 classes. Each class contains 6000 images in size 32 32. There are 5000 training images and 1000 testing images per class. ... Image Net dataset (ILSVRC2012) has about 1.2M training images from 1000 classes and 50,000 validation images. |
| Hardware Specification | Yes | All experiments are conducted on a GPU Server which has 8 Nvidia Titan Xp GPUs. |
| Software Dependencies | No | The paper mentions using "Caffe framework" and "CUDA" but does not specify version numbers for these software components. |
| Experiment Setup | Yes | We implement our proposed method based on the Caffe framework... We adopt different fine-tuning settings for different network architecture. Alex Net We fine-tune Alex Net using a SGD solver with momentum=0.9, weight decay=0.0005. The learning rate starts at 0.001, and is divided by 10 after 100k, 150k, and 180k iterations. The network is fine-tuned for 200k iterations with batch-size equals to 256. |